The embodiments described herein relate generally to image data collection and analysis systems, and, more particularly, to an image detection assembly that can be used with such systems for the determination of transient effects on components or devices, such as integrated circuits (“IC”).
It is advantageous to collect and analyze image data in an automated fashion when comparing the image of a product to an ideal product image, or for detecting changes in a product from transient events or effects. The product in question could be an IC, for example, and comparison of the conductors and vias visible on the surface of the IC to those of an ideal image may reveal contaminants, manufacturing defects, physical damage, such as radiation damage, or even the presence of spurious circuitry (e.g., a hardware version of malware hidden therein). Other products may also be advantageously imaged for purposes of discriminating for changes, comparing an image to an expected image, and generally analyzing the visible features of a product by collecting and processing an image.
Various image and other surface detection and characterization techniques can be used to identify features, such as defects. Some optoelectronic data collection and image characterization techniques are known for collecting and assessing data representing the appearance, spatial characteristics, and changes in the appearance or characteristics of ICs. However, an image characterization that is sufficiently detailed to enable identification of a single event upset (“SEU”), such as a point of isolated radiation damage, that might have occurred at any location on the area of the IC, requires collection of data at all points of the IC where the damage might have occurred. One might collect a high resolution pixel image under illumination, for example by scanning a laser over each part of the IC in a raster, and collecting and digitizing the reflected amplitude at each pixel position. However, such a technique can be time consuming and produce a great deal of pixel data. Therefore, such known techniques can be inefficient, can consume time and resources, and are subject to systematic errors.